Ebook: Gaussian Random Functions

It is well known that the normal distribution is the most pleasant, one can even say, an exemplary object in the probability theory. It combines almost all conceivable nice properties that a distribution may ever have: symmetry, stability, indecomposability, a regular tail behavior, etc. Gaussian measures (the distributions of Gaussian random functions), as infinite-dimensional analogues of tht< classical normal distribution, go to work as such exemplary objects in the theory of Gaussian random functions. When one switches to the infinite dimension, some "one-dimensional" properties are extended almost literally, while some others should be profoundly justified, or even must be reconsidered. What is more, the infinite-dimensional situation reveals important links and structures, which either have looked trivial or have not played an independent role in the classical case. The complex of concepts and problems emerging here has become a subject of the theory of Gaussian random functions and their distributions, one of the most advanced fields of the probability science. Although the basic elements in this field were formed in the sixties-seventies, it has been still until recently when a substantial part of the corresponding material has either existed in the form of odd articles in various journals, or has served only as a background for considering some special issues in monographs.

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Content:
Front Matter....Pages i-xi
Gaussian Distributions and Random Variables....Pages 1-7
Multi-Dimensional Gaussian Distributions....Pages 8-15
Covariances....Pages 16-21
Random Functions....Pages 22-29
Examples of Gaussian Random Functions....Pages 30-40
Modelling the Covariances....Pages 41-52
Oscillations....Pages 53-67
Infinite-Dimensional Gaussian Distributions....Pages 68-83
Linear Functionals, Admissible Shifts, and the Kernel....Pages 84-100
The Most Important Gaussian Distributions....Pages 101-107
Convexity and the Isoperimetric Property....Pages 108-138
The Large Deviations Principle....Pages 139-155
Exact Asymptotics of Large Deviations....Pages 156-176
Metric Entropy and the Comparison Principle....Pages 177-210
Continuity and Boundedness....Pages 211-229
Majorizing Measures....Pages 230-245
The Functional Law of the Iterated Logarithm....Pages 246-257
Small Deviations....Pages 258-275
Several Open Problems....Pages 276-281
Back Matter....Pages 282-337

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Content:
Front Matter....Pages i-xi
Gaussian Distributions and Random Variables....Pages 1-7
Multi-Dimensional Gaussian Distributions....Pages 8-15
Covariances....Pages 16-21
Random Functions....Pages 22-29
Examples of Gaussian Random Functions....Pages 30-40
Modelling the Covariances....Pages 41-52
Oscillations....Pages 53-67
Infinite-Dimensional Gaussian Distributions....Pages 68-83
Linear Functionals, Admissible Shifts, and the Kernel....Pages 84-100
The Most Important Gaussian Distributions....Pages 101-107
Convexity and the Isoperimetric Property....Pages 108-138
The Large Deviations Principle....Pages 139-155
Exact Asymptotics of Large Deviations....Pages 156-176
Metric Entropy and the Comparison Principle....Pages 177-210
Continuity and Boundedness....Pages 211-229
Majorizing Measures....Pages 230-245
The Functional Law of the Iterated Logarithm....Pages 246-257
Small Deviations....Pages 258-275
Several Open Problems....Pages 276-281
Back Matter....Pages 282-337
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